Gene-based Therapeutic Tools in the Treatment of Cornea Disease

Genome Editing VEGFA Prevents Corneal Neovascularization In Vivo

Corneal neovascularization (CNV) is a common clinical finding seen in a range of eye diseases. Current therapeutic approaches to treat corneal angiogenesis, in which vascular endothelial growth factor (VEGF) A plays a central role, can cause a variety of adverse side effects. The technology of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 can edit VEGFA gene to suppress its expression. CRISPR offers a novel opportunity to treat CNV. This study shows that depletion of VEGFA with a novel CRISPR/Cas9 system inhibits proliferation, migration, and tube formation of human umbilical vein endothelial cells (HUVECs) in vitro. Importantly, subconjunctival injection of this dual AAV-SpCas9/sgRNA-VEGFA system is demonstrated which blocks suture-induced expression of VEGFA, CD31, and α-smooth muscle actin as well as corneal neovascularization in mice. This study has established a strong foundation for the treatment of corneal neovascularization via a gene editing approach for the first time.

Application of Sparse Representation in Bioinformatics

Inspired by L1-norm minimization methods, such as basis pursuit, compressed sensing, and Lasso feature selection, in recent years, sparse representation shows up as a novel and potent data processing method and displays powerful superiority. Researchers have not only extended the sparse representation of a signal to image presentation, but also applied the sparsity of vectors to that of matrices. Moreover, sparse representation has been applied to pattern recognition with good results. Because of its multiple advantages, such as insensitivity to noise, strong robustness, less sensitivity to selected features, and no “overfitting” phenomenon, the application of sparse representation in bioinformatics should be studied further. This article reviews the development of sparse representation, and explains its applications in bioinformatics, namely the use of low-rank representation matrices to identify and study cancer molecules, low-rank sparse representations to analyze and process gene expression profiles, and an introduction to related cancers and gene expression profile database.

iTTCA-RF: a random forest predictor for tumor T cell antigens

BACKGROUND

Cancer is one of the most serious diseases threatening human health. Cancer immunotherapy represents the most promising treatment strategy due to its high efficacy and selectivity and lower side effects compared with traditional treatment. The identification of tumor T cell antigens is one of the most important tasks for antitumor vaccines development and molecular function investigation. Although several machine learning predictors have been developed to identify tumor T cell antigen, more accurate tumor T cell antigen identification by existing methodology is still challenging.

METHODS

In this study, we used a non-redundant dataset of 592 tumor T cell antigens (positive samples) and 393 tumor T cell antigens (negative samples). Four types feature encoding methods have been studied to build an efficient predictor, including amino acid composition, global protein sequence descriptors and grouped amino acid and peptide composition. To improve the feature representation ability of the hybrid features, we further employed a two-step feature selection technique to search for the optimal feature subset. The final prediction model was constructed using random forest algorithm.

RESULTS

Finally, the top 263 informative features were selected to train the random forest classifier for detecting tumor T cell antigen peptides. iTTCA-RF provides satisfactory performance, with balanced accuracy, specificity and sensitivity values of 83.71%, 78.73% and 88.69% over tenfold cross-validation as well as 73.14%, 62.67% and 83.61% over independent tests, respectively. The online prediction server was freely accessible at http://lab.malab.cn/~acy/iTTCA .

CONCLUSIONS

We have proven that the proposed predictor iTTCA-RF is superior to the other latest models, and will hopefully become an effective and useful tool for identifying tumor T cell antigens presented in the context of major histocompatibility complex class I.

Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data

Gastric cancer (GC) is one of the most common causes of cancer-related deaths in the world. This cancer has been regarded as a biological and genetically heterogeneous disease with a poorly understood carcinogenesis at the molecular level. Thousands of biomarkers and susceptible loci have been explored via experimental and computational methods, but their effects on disease outcome are still unknown. Genome-wide association studies (GWAS) have identified multiple susceptible loci for GC, but due to the linkage disequilibrium (LD), single-nucleotide polymorphisms (SNPs) may fall within the non-coding region and exert their biological function by modulating the gene expression level. In this study, we collected 1,091 cases and 410,350 controls from the GWAS catalog database. Integrating with gene expression level data obtained from stomach tissue, we conducted a machine learning-based method to predict GC-susceptible genes. As a result, we identified 787 novel susceptible genes related to GC, which will provide new insight into the genetic and biological basis for the mechanism and pathology of GC development.

A Mendelian Randomization Study on Infant Length and Type 2 Diabetes Mellitus Risk

OBJECTIVE

Infant length (IL) is a positively associated phenotype of type 2 diabetes mellitus (T2DM), but the causal relationship of which is still unclear. Here, we applied a Mendelian randomization (MR) study to explore the causal relationship between IL and T2DM, which has the potential to provide guidance for assessing T2DM activity and T2DM- prevention in young at-risk populations.

MATERIALS AND METHODS

To classify the study, a two-sample MR, using genetic instrumental variables (IVs) to explore the causal effect was applied to test the influence of IL on the risk of T2DM. In this study, MR was carried out on GWAS data using 8 independent IL SNPs as IVs. The pooled odds ratio (OR) of these SNPs was calculated by the inverse-variance weighted method for the assessment of the risk the shorter IL brings to T2DM. Sensitivity validation was conducted to identify the effect of individual SNPs. MR-Egger regression was used to detect pleiotropic bias of IVs.

RESULTS

The pooled odds ratio from the IVW method was 1.03 (95% CI 0.89-1.18, P = 0.0785), low intercept was -0.477, P = 0.252, and small fluctuation of ORs ranged from -0.062 ((0.966 - 1.03) / 1.03) to 0.05 ((1.081 - 1.03) / 1.03) in leave-one-out validation.

CONCLUSION

We validated that the shorter IL causes no additional risk to T2DM. The sensitivity analysis and the MR-Egger regression analysis also provided adequate evidence that the above result was not due to any heterogeneity or pleiotropic effect of IVs.

MMP-9 Downregulation with Lipid Nanoparticles for Inhibiting Corneal Neovascularization by Gene Silencing

Gene silencing targeting proangiogenic factors have been shown to be a useful strategy in the treatment of corneal neovascularization (CNV). Among interference RNA (RNAi) molecules, short-hairpin RNA (shRNA) is a plasmid-coded RNA able to down-regulate the expression of the desired gene. It is continuously produced in the host cell, inducing a durable gene silencing effect. The aim of this work was to develop a solid lipid nanoparticle (SLN)-based shRNA delivery system to downregulate metalloproteinase 9 (MMP-9), a proangiogenic factor, in corneal cells for the treatment of CNV associated with inflammation. The nanovectors were prepared using a solvent emulsification-evaporation technique, and after physicochemical evaluation, they were evaluated in different culture cell models. Transfection efficacy, cell internalization, cell viability, the effect on MMP-9 expression, and cell migration were evaluated in human corneal epithelial cells (HCE-2). The inhibition of tube formation using human umbilical vein endothelial cells (HUVEC) was also assayed. The non-viral vectors based on SLN were able to downregulate the MMP-9 expression in HCE-2 cells via gene silencing, and, consequently, to inhibit cell migration and tube formation. These results demonstrate the potential of lipid nanoparticles as gene delivery systems for the treatment of CNV-associated inflammation by RNAi technology.